FedMI: Reliable and Privacy-Aware Vertical Federated Learning for Anomaly Detection in Distributed Edge Systems

Anomaly detection in distributed systems faces critical challenges from feature heterogeneity—where incomplete or divergent feature sets across nodes degrade detection relia-bility—and privacy risks under regulations like GDPR. While federated learning (FL) enables collaborative training without raw data sharing, existing solutions fail to address both challenges simultaneously: traditional FL suffers from performance drops under feature-missing scenarios, and differential privacy techniques introduce utility penalties. This paper proposes FedMI, a vertical federated learning framework that achieves provable privacy preservation and robust anomaly detection in feature-heterogeneous environments. FedMI's key innovations include a novel framework for vertical federated learning in anomaly detection for distributed systems that maintains high detection accuracy, mimicking real-world distributed system conditions, and a mutual information-guided training mechanism that quantifies and minimizes privacy leakage during federated updates. Evaluations on healthcare, financial, and industrial sensor datasets demonstrate FedMI's robustness: it achieves performance comparable to centralized methods in F1-score under data-island scenarios while ensuring compliance with privacy constraints. By unifying privacy quantification and robustness to feature heterogeneity, FedMI advances the development of dependable AI-driven monitoring for distributed systems.